Implementasi Tabu Search Pada Heterogeneous Fleet Vehicle Routing Problem with Time Windows

ABSTRAKSI: Vehicle Routing Problem (VRP) adalah permasalahan optimasi rute kendaraan yang dimulai dari depot, menelusuri sebuah subset dari pelanggan dalam urutan tertentu dan kembali ke depot menggunakan kendaraan dengan kapasitas tertentu. Heterogeneous Fleet Vehicle Routing Problem with Time Windows (HFVRPTW) adalah varian dari VRP yang memiliki kriteria tambahan, yaitu kapasitas kendaraan yang digunakan dapat berbeda-beda serta memiliki time windows atau interval waktu pengiriman baik di sisi pelanggan maupun di sisi depot.Pada tugas akhir ini, pencarian jalur pada kasus HFVRPTW akan diselesaikan menggunakan Tabu Search (TS). TS digunakan untuk menghindari terperangkap dalam solusi optimal yang bersifat lokal.Pengujian dilakukan untuk mencari parameter-parameter terbaik untuk mendapatkan solusi yang mendekati optimal. Dari hasil pengujian, diketahui bahwa jumlah iterasi mempengaruhi solusi yang dihasilkan. Semakin besar iterasi, hasil yang diperoleh semakin optimal.Kata Kunci : Heterogeneous Fleet Vehicle Routing Problem with Time Windows (HFVRPTW), Tabu Search, pencarian rute.ABSTRACT: Vehicle Routing Problem (VRP) is a route optimization that starts from the depot, traverses a subset of customers in a certain order and returned to the depot using vehicles with certain capacities. Heterogeneous Fleet Vehicle Routing Problem with Time Windows (HFVRPTW) is a variant of the VRP that have additional criteria, differences in the capacity of the vehicle and has time windows or time interval delivery in both the customers and depot.In this final project, to find optimal route in case HFVRPTW will be solved using the Tabu Search (TS). TS is used to avoid trapped in locally optimal solutions.Tests conducted to find the best parameters to obtain an optimal solution. From the test results, it is known that the number of iterations affects the resulting solution. The greater the iterations, the optimal results obtained.Keyword: Heterogeneous Fleet Vehicle Routing Problem with Time Windows (HFVRPTW), Tabu Search, route searching

A Parallel Monte-Carlo Tree Search-Based Metaheuristic For Optimal Fleet Composition Considering Vehicle Routing Using Branch & Bound

In this paper, a Monte-Carlo Tree Search (MCTS)-based metaheuristic is developed that guides a Branch & Bound (B&B) algorithm to find the globally optimal solution to the heterogeneous fleet composition problem while considering vehicle routing. Fleet Size and Mix Vehicle Routing Problem with Time Windows (FSMVRPTW). The metaheuristic and exact algorithms are implemented in a parallel hybrid optimization algorithm where the metaheuristic rapidly finds feasible solutions that provide candidate upper bounds for the B&B algorithm which runs simultaneously. The MCTS additionally provides a candidate fleet composition to initiate the B&B search. Experiments show that the proposed approach results in significant improvements in computation time and convergence to the optimal solution.Comment: Submitted to the IEEE Intelligent Vehicles Symposium 202

A Column Generation for the Heterogeneous Fixed Fleet Open Vehicle Routing Problem

[EN] This paper addressed the heterogeneous fixed fleet open vehicle routing problem (HFFOVRP), in which the vehicles are not required to return to the depot after completing a service. In this new problem, the demands of customers are fulfilled by a heterogeneous fixed fleet of vehicles having various capacities, fixed costs and variable costs. This problem is an important variant of the open vehicle routing problem (OVRP) and can cover more practical situations in transportation and logistics. Since this problem belongs to NP-hard Problems, An approach based on column generation (CG) is applied to solve the HFFOVRP. A tight integer programming model is presented and the linear programming relaxation of which is solved by the CG technique. Since there have been no existing benchmarks, this study generated 19 test problems and the results of the proposed CG algorithm is compared to the results of exact algorithm. Computational experience confirms that the proposed algorithm can provide better solutions within a comparatively shorter period of time.Yousefikhoshbakht, M.; Dolatnejad, A. (2017). A Column Generation for the Heterogeneous Fixed Fleet Open Vehicle Routing Problem. International Journal of Production Management and Engineering. 5(2):55-71. doi:10.4995/ijpme.2017.5916SWORD557152Aleman, R. E., & Hill, R. R. (2010). A tabu search with vocabulary building approach for the vehicle routing problem with split demands. International Journal of Metaheuristics, 1(1), 55. doi:10.1504/ijmheur.2010.033123Anbuudayasankar, S. P., Ganesh, K., Lenny Koh, S. C., & Ducq, Y. (2012). Modified savings heuristics and genetic algorithm for bi-objective vehicle routing problem with forced backhauls. Expert Systems with Applications, 39(3), 2296-2305. doi:10.1016/j.eswa.2011.08.009Brandão, J. (2009). A deterministic tabu search algorithm for the fleet size and mix vehicle routing problem. European Journal of Operational Research, 195(3), 716-728. doi:10.1016/j.ejor.2007.05.059Çatay, B. (2010). A new saving-based ant algorithm for the Vehicle Routing Problem with Simultaneous Pickup and Delivery. Expert Systems with Applications, 37(10), 6809-6817. doi:10.1016/j.eswa.2010.03.045Dantzig, G. B., & Ramser, J. H. (1959). The Truck Dispatching Problem. Management Science, 6(1), 80-91. doi:10.1287/mnsc.6.1.80Gendreau, M., Guertin, F., Potvin, J.-Y., & Séguin, R. (2006). Neighborhood search heuristics for a dynamic vehicle dispatching problem with pick-ups and deliveries. Transportation Research Part C: Emerging Technologies, 14(3), 157-174. doi:10.1016/j.trc.2006.03.002Gendreau, M., Laporte, G., Musaraganyi, C., & Taillard, É. D. (1999). A tabu search heuristic for the heterogeneous fleet vehicle routing problem. Computers & Operations Research, 26(12), 1153-1173. doi:10.1016/s0305-0548(98)00100-2Lei, H., Laporte, G., & Guo, B. (2011). The capacitated vehicle routing problem with stochastic demands and time windows. Computers & Operations Research, 38(12), 1775-1783. doi:10.1016/j.cor.2011.02.007Li, X., Leung, S. C. H., & Tian, P. (2012). A multistart adaptive memory-based tabu search algorithm for the heterogeneous fixed fleet open vehicle routing problem. Expert Systems with Applications, 39(1), 365-374. doi:10.1016/j.eswa.2011.07.025Li, X., Tian, P., & Aneja, Y. P. (2010). An adaptive memory programming metaheuristic for the heterogeneous fixed fleet vehicle routing problem. Transportation Research Part E: Logistics and Transportation Review, 46(6), 1111-1127. doi:10.1016/j.tre.2010.02.004Penna, P. H. V., Subramanian, A., & Ochi, L. S. (2011). An Iterated Local Search heuristic for the Heterogeneous Fleet Vehicle Routing Problem. Journal of Heuristics, 19(2), 201-232. doi:10.1007/s10732-011-9186-ySaadati Eskandari, Z., YousefiKhoshbakht, M. (2012). Solving the Vehicle Routing Problem by an Effective Reactive Bone Route Algorithm, Transportation Research Journal, 1(2), 51-69.Subramanian, A., Drummond, L. M. A., Bentes, C., Ochi, L. S., & Farias, R. (2010). A parallel heuristic for the Vehicle Routing Problem with Simultaneous Pickup and Delivery. Computers & Operations Research, 37(11), 1899-1911. doi:10.1016/j.cor.2009.10.011Syslo, M., Deo, N., Kowalik, J. (1983). Discrete Optimization Algorithms with Pascal Programs, Prentice Hall.Taillard, E. D. (1999). A heuristic column generation method for the heterogeneous fleet VRP, RAIRO Operations Research, 33, 1-14. https://doi.org/10.1051/ro:1999101Tarantilis, C. D., & Kiranoudis, C. T. (2007). A flexible adaptive memory-based algorithm for real-life transportation operations: Two case studies from dairy and construction sector. European Journal of Operational Research, 179(3), 806-822. doi:10.1016/j.ejor.2005.03.059Wang, H.-F., & Chen, Y.-Y. (2012). A genetic algorithm for the simultaneous delivery and pickup problems with time window. Computers & Industrial Engineering, 62(1), 84-95. doi:10.1016/j.cie.2011.08.018Yousefikhoshbakht, M., Didehvar, F., & Rahmati, F. (2013). Solving the heterogeneous fixed fleet open vehicle routing problem by a combined metaheuristic algorithm. International Journal of Production Research, 52(9), 2565-2575. doi:10.1080/00207543.2013.855337Yousefikhoshbakht, M., & Khorram, E. (2012). Solving the vehicle routing problem by a hybrid meta-heuristic algorithm. Journal of Industrial Engineering International, 8(1). doi:10.1186/2251-712x-8-1

A parallel matheuristic for the technician routing problem with electric and conventional vehicles

The technician routing problem with conventional and electric vehicles (TRP-CEV) consists in designing service routes taking into account the customers’ time windows and the technicians’ skills, shifts, and lunch breaks. In the TRP-CEV routes are covered using a fixed and heterogeneous fleet of conventional and electric vehicles (EVs). Due to their relatively limited driving ranges, EVs may need to include in their routes one or more recharging stops. In this talk we present a parallel matheuristic for the TRP-CEV. The approach works in two phases. In the first phase it decomposes the problem into a number of “easier to solve” vehicle routing problems with time windows and solves these problems in parallel using a GRASP. During the execution of this phase, the routes making up the local optima are stored in a long-term memory. In the second phase, the approach uses the routes stored in the long-term memory to assemble a solution to the TRP-CEV. We discuss computational experiments carried on real-world TRP-CEV instances provided by a French public utility and instances for the closely-related electric fleet size and mix vehicle routing problem with time windows and recharging stations taken from the literature.

A Granular Tabu Search Algorithm for a Real Case Study of a Vehicle Routing Problem with a Heterogeneous Fleet and Time Windows

Purpose: We consider a real case study of a vehicle routing problem with a heterogeneous fleet and time windows (HFVRPTW) for a franchise company bottling Coca-Cola products in Colombia. This study aims to determine the routes to be performed to fulfill the demand of the customers by using a heterogeneous fleet and considering soft time windows. The objective is to minimize the distance traveled by the performed routes. Design/methodology/approach: We propose a two-phase heuristic algorithm. In the proposed approach, after an initial phase (first phase), a granular tabu search is applied during the improvement phase (second phase). Two additional procedures are considered to help that the algorithm could escape from local optimum, given that during a given number of iterations there has been no improvement. Findings: Computational experiments on real instances show that the proposed algorithm is able to obtain high-quality solutions within a short computing time compared to the results found by the software that the company currently uses to plan the daily routes. Originality/value: We propose a novel metaheuristic algorithm for solving a real routing problem by considering heterogeneous fleet and time windows. The efficiency of the proposed approach has been tested on real instances, and the computational experiments shown its applicability and performance for solving NP-Hard Problems related with routing problems with similar characteristics. The proposed algorithm was able to improve some of the current solutions applied by the company by reducing the route length and the number of vehicles.Peer Reviewe

Waste Collection Vehicle Routing Problem Model with Multiple Trips, Time Windows, Split Delivery, Heterogeneous Fleet and Intermediate Facility

Waste Collection Vehicle Routing Problem (WCVRP) is one of the developments of a Vehicle Routing Problem, which can solve the route determination of transporting waste. This study aims to develop a model from WCVRP by adding characteristics such as split delivery, multiple trips, time windows, heterogeneous fleet, and intermediate facilities alongside an objective function to minimize costs and travel distance. Our model determines the route for transporting waste especially in Cakung District, East Jakarta. The additional characteristics are obtained by analyzing the characteristics of waste transportation in the area. The models are tested using dummy data to analyze the required computational time and route suitability. The models contribute to determining the route of transporting waste afterward. The WCVRP model has been successfully developed, conducted the numerical testing, and implemented with the actual characteristics such as split delivery, multiple trips, time windows, heterogeneous fleets, and intermediate facilities. The output has reached the global optimal for both dummy and real data

A Capacitated Heterogeneous Vehicle Routing Problem for Catering Service Delivery with Committed Scheduled Time

The heterogeneous vehicle routing problem (HVRP) is a well-known combinatorial optimization problem which describes a heterogeneous set of vehicles with different capacity, in which each vehicle starts from a central depot and traverses along a route in order to serve a set of customers with known geographical locations. This paper develops a model for the optimal management of service deliveries of meals of a catering company located in Medan City, Indonesia. The HVRP incorporates time windows, deliveries, fleet scheduling in the committed scheduled time planning.. The objective is to minimize the sum of the costs of travelling and elapsed time over the planning horizon. We model the problem as a linear mixed integer program and we propose a feasible neighbourhood direct search approach to solve the problem

A Bucket Graph Based Labelling Algorithm for Vehicle Routing

International audienceWe consider the Shortest Path Problem with Resource Constraints (SPPRC) arising as a subproblem in state-of-the-art Branch-Cut-and-Price algorithms for vehicle routing problems. We propose a variant of the bi-directional label correcting algorithm in which the labels are stored and extended according to the so-called bucket graph. Such organization of labels helps to decrease significantly the number of dominance checks and the running time of the algorithm. We also show how the forward/backward route symmetry can be exploited and how to eliminate arcs from the bucket graph using reduced costs. The proposed algorithm can be especially beneficial for vehicle routing instances with large vehicle capacity and/or with time window constraints. Computational experiments were performed on instances from the distance constrained vehicle routing problem, including multi-depot and site-dependent variants, on the vehicle routing problem with time windows, and on the "nightmare" instances of the heterogeneous fleet vehicle routing problem. Significant improvements over the best algorithms in the literature were achieved and many instances could be solved for the first time